This Paper introduces transformAR, a lightweight transformer-based model for objective quality assessment in AR applications. This approach utilizes pre-trained vision transformer-based encoders to capture image content information, computes distance vectors for quantifying distortions, and employs cross-attention-based decoders to model perceptual quality features. The model integrates adapted regularization techniques and label smoothing to mitigate overfitting. Experimental results demonstrate the effectiveness of transformAR, surpassing existing state-of-the-art methods.